1 Load environment

Code

import warnings

warnings.filterwarnings("ignore")

import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import pandas as pd
import numpy as np
import random
import itertools

from tqdm import tqdm

import decoupler as dc
import sys

sys.path.append("./../../../../utilities_folder")
from utilities import load_object, intTable, plotGenesInTerm, getAnnGenes, run_ora_catchErrors

Set R environment with rpy2:

Code

import rpy2.rinterface_lib.callbacks
import anndata2ri
import logging

from rpy2.robjects import pandas2ri
import rpy2.robjects as ro

sc.settings.verbosity = 0
rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR)

pandas2ri.activate()
anndata2ri.activate()

%load_ext rpy2.ipython

Set up of graphical parameters for Python plots:

Code

%matplotlib inline
sc.set_figure_params(dpi = 300, fontsize = 20)

cmap_up = sns.light_palette("red", as_cmap=True)
cmap_down = sns.light_palette("blue", as_cmap=True)
cmap_all = sns.light_palette("seagreen", as_cmap=True)

Set up of graphical parameters for R plots:

Code

default_units = 'in' 
default_res = 300
default_width = 10
default_height = 9

import rpy2
old_setup_graphics = rpy2.ipython.rmagic.RMagics.setup_graphics

def new_setup_graphics(self, args):
    if getattr(args, 'units') is not None:
        if args.units != default_units:
            return old_setup_graphics(self, args)
    args.units = default_units
    if getattr(args, 'res') is None:
        args.res = default_res
    if getattr(args, 'width') is None:
        args.width = default_width
    if getattr(args, 'height') is None:
        args.height = default_height        
    return old_setup_graphics(self, args)


rpy2.ipython.rmagic.RMagics.setup_graphics = new_setup_graphics

Here the cell were we inject the parameters using Quarto renderer:

Code

# Injected Parameters
N = 3

Code

# Injected Parameters
N = 2

Import R libraries:

Code

%%R
source('./../../../../utilities_folder/GO_helper.r')
loc <- './../../../../R_loc' # pointing to the renv environment

.libPaths(loc)

library('topGO')
library('org.Hs.eg.db')
library(dplyr)
library(ggplot2)

Set output folders:

Code

output_folder = './'
folder = './deg_in_cluster_tables/cluster_' + str(N) + '/'

import os

if not os.path.isdir(folder):
    os.makedirs(folder)

2 Load data

Here we load the anndata:

Code

GO2gene = load_object('./../../../../data/GO2gene_complete.pickle')

Here we load the dictionary that associates to each GO term its genes:

Code

adata = sc.read("1_All_Annotated_Triku.h5ad")
adata

AnnData object with n_obs × n_vars = 27925 × 14582
    obs: 'sample_id', 'run_id', 'probe_barcode_ids', 'subject', 'line', 'specimen', 'stage', 'condition', 'notes', 'seqRun', 'original_name', 'project', 'who', 'SC_derivation', 'micoplasma', 'mosaic', 'genSite', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mito', 'log1p_total_counts_mito', 'pct_counts_mito', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'gene_UMI_ratio', 'log1p_gene_UMI_ratio', 'scDblFinder_score', 'scDblFinder_class', 'n_counts', 'n_genes', 'leiden', 'score_pluripotency', 'score_mesoderm', 'score_PS', 'score_EM', 'score_AM', 'score_Endo', 'score_Epi', 'score_HEP', 'score_NM', 'score_ExM', 'score_Ery', 'score_EAE', 'score_Amnion_LC', 'score_AxM', 'score_Amnion', 'score_NNE', 'score_PGC', 'score_hPGCLCs', 'score_DE1', 'score_DE2', 'score_Hypoblast', 'score_YS', 'score_EMPs', 'score_Endothelium', 'score_Myeloid_Progenitors', 'score_Megakaryocyte_Erythroid_progenitors', 'CellTypes'
    var: 'n_cells', 'mito', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection', 'triku_distance', 'triku_distance_uncorrected', 'triku_highly_variable'
    uns: 'CellTypes_colors', 'dendrogram_CellTypes', 'dendrogram_leiden', 'dendrogram_specimen', 'hvg', 'leiden', 'leiden_colors', 'log1p', 'neighbors', 'pca', 'rank_genes_groups', 'run_id_colors', 'sample_id_colors', 'specimen_colors', 'stage_colors', 'triku_params', 'umap'
    obsm: 'X_pca', 'X_pca_harmony', 'X_umap'
    varm: 'PCs'
    layers: 'raw'
    obsp: 'connectivities', 'distances'

3 Markers of cluster

We filter genes for the cluster under investigation based on the p-value adjusted that we then convert in -log(p-value adjusted):

Code

markers = sc.get.rank_genes_groups_df(adata, group=str(N))
intTable(markers, folder=folder, fileName = 'markers_cluster_before_filtering_' + str(N) + '.xlsx', save = True)

Code

markers = markers.set_index('names')
markers = markers[markers.pvals_adj < 0.01]
markers['FDR'] = markers['pvals_adj']
markers['-log10(FDR)'] = -np.log10(markers.pvals_adj)
markers = markers.replace(np.inf, markers[markers['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
markers

	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group	pct_nz_reference	FDR	-log10(FDR)
names
COL4A2	93.131279	4.240220	0.0	0.0	1.000000	0.889565	0.0	309.013678
COL4A1	92.239578	4.473654	0.0	0.0	0.999710	0.859372	0.0	309.013678
ZEB2	91.778168	6.360259	0.0	0.0	0.994781	0.168001	0.0	309.013678
COL3A1	91.229744	8.745208	0.0	0.0	1.000000	0.238765	0.0	309.013678
MAN1A1	90.926048	5.642728	0.0	0.0	0.996521	0.312592	0.0	309.013678
...	...	...	...	...	...	...	...	...
SLC7A5	-82.212738	-4.583455	0.0	0.0	0.380400	0.923803	0.0	309.013678
CLDN6	-83.345406	-7.369551	0.0	0.0	0.140041	0.895735	0.0	309.013678
CDH3	-83.556168	-4.574719	0.0	0.0	0.246448	0.929114	0.0	309.013678
SLC38A1	-84.280098	-4.575012	0.0	0.0	0.294578	0.935284	0.0	309.013678
PODXL	-88.903313	-6.873848	0.0	0.0	0.209336	0.957019	0.0	309.013678

11525 rows × 8 columns

Code

intTable(markers, folder=folder, fileName = 'markers_cluster_filtered_' + str(N) + '.xlsx', save = True)

3.1 Select up and downregulated markers

3.1.1 Up

Code

pos = markers[markers.logfoldchanges > 1]
pos

	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group	pct_nz_reference	FDR	-log10(FDR)
names
COL4A2	93.131279	4.240220	0.000000	0.000000	1.000000	0.889565	0.000000	309.013678
COL4A1	92.239578	4.473654	0.000000	0.000000	0.999710	0.859372	0.000000	309.013678
ZEB2	91.778168	6.360259	0.000000	0.000000	0.994781	0.168001	0.000000	309.013678
COL3A1	91.229744	8.745208	0.000000	0.000000	1.000000	0.238765	0.000000	309.013678
MAN1A1	90.926048	5.642728	0.000000	0.000000	0.996521	0.312592	0.000000	309.013678
...	...	...	...	...	...	...	...	...
RAI2	2.689245	1.167971	0.007161	0.009085	0.055668	0.027701	0.009085	2.041657
GRAP2	2.687262	3.175457	0.007204	0.009135	0.031893	0.003677	0.009135	2.039304
ZNF575	2.676554	1.543274	0.007438	0.009427	0.044361	0.016302	0.009427	2.025631
SLC45A2	2.666203	2.808888	0.007671	0.009716	0.032473	0.004453	0.009716	2.012500
CHI3L1	2.660832	1.351641	0.007795	0.009868	0.046100	0.018140	0.009868	2.005756

1802 rows × 8 columns

Code

pos['-log10(FDR)'] = -np.log10(pos.pvals_adj)
#pos = pos.fillna('inf')
pos = pos.replace(np.inf, pos[pos['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
pos['FDR'] = pos['pvals_adj']
up_regGenes = pos.index.tolist()
allUpSelected = adata.var_names.isin(up_regGenes).astype('int').tolist()

3.1.2 Down

Code

neg = markers[markers.logfoldchanges < -1].sort_values(by = 'logfoldchanges')
neg

	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group	pct_nz_reference	FDR	-log10(FDR)
names
FOXB1	-11.905676	-27.653414	1.105636e-32	2.346097e-32	0.000000	0.125020	2.346097e-32	31.629654
SLC52A3	-12.438708	-27.555775	1.610885e-35	3.516982e-35	0.000000	0.130618	3.516982e-35	34.453830
KCNJ11	-11.820080	-27.438763	3.073928e-32	6.493410e-32	0.000000	0.124122	6.493410e-32	31.187527
BICDL2	-11.742265	-27.394825	7.738591e-32	1.629518e-31	0.000000	0.123304	1.629518e-31	30.787941
LGALS14	-6.874944	-26.727528	6.201408e-12	1.009251e-11	0.000000	0.072193	1.009251e-11	10.996001
...	...	...	...	...	...	...	...	...
DESI2	-32.305519	-1.001727	5.851790e-229	3.426940e-228	0.660191	0.824359	3.426940e-228	227.465094
NAP1L4	-40.579399	-1.001709	0.000000e+00	0.000000e+00	0.807770	0.925764	0.000000e+00	309.013678
RAB5IF	-31.241621	-1.001236	2.901493e-214	1.633574e-213	0.621919	0.798905	1.633574e-213	212.786861
C9orf16	-34.725502	-1.000740	3.248261e-264	2.191862e-263	0.733256	0.873958	2.191862e-263	262.659187
CBLL1	-21.161049	-1.000238	2.183095e-99	7.333308e-99	0.358075	0.549109	7.333308e-99	98.134700

3637 rows × 8 columns

Code

neg['-log10(FDR)'] = -np.log10(neg.pvals_adj)
#neg = neg.fillna('inf')
neg = neg.replace(np.inf, neg[neg['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
neg['FDR'] = neg['pvals_adj']
down_regGenes = neg.index.tolist()
allDownSelected = adata.var_names.isin(down_regGenes).astype('int').tolist()

3.1.3 All regulated

Code

all_sign = up_regGenes + down_regGenes
allSelected = adata.var_names.isin(all_sign).astype('int').tolist()
allGenes = adata.var_names.tolist()

4 topGO

4.1 Upregulated

Code

%%R -i allUpSelected -i allGenes

allGenes_v <- c(allUpSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Get topGO results:

Code

%%R -o results

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Convert results from R environment to Python:

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13071,  1638,    10,  5722], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

results_table_py = ro.conversion.rpy2py(results_table)

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0030199	collagen fibril organization	60	37	7.52	3.7e-19	3.692981e-19
1	GO:0030198	extracellular matrix organization	267	120	33.46	3.3e-17	3.278726e-17
2	GO:0001525	angiogenesis	450	137	56.39	4.0e-11	4.019062e-11
3	GO:0007155	cell adhesion	1176	283	147.37	1.1e-09	1.142650e-09
4	GO:0045766	positive regulation of angiogenesis	139	45	17.42	1.4e-09	1.440942e-09
...	...	...	...	...	...	...	...
6269	GO:2000601	positive regulation of Arp2/3 complex-me...	10	0	1.25	1.00000	1.000000e+00
6270	GO:2000765	regulation of cytoplasmic translation	26	0	3.26	1.00000	1.000000e+00
6271	GO:2000767	positive regulation of cytoplasmic trans...	13	0	1.63	1.00000	1.000000e+00
6272	GO:2001138	regulation of phospholipid transport	12	0	1.50	1.00000	1.000000e+00
6273	GO:2001140	positive regulation of phospholipid tran...	11	0	1.38	1.00000	1.000000e+00

6274 rows × 7 columns

Filter results:

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_up.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'red')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='red', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_up.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13424,  1667,    10,   999], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_up.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'red')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='red', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_up.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13666,  1716,    10,   656], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_up.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'red')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='red', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_up.xlsx'), SE = markers)

Pathway annotation

4.1.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
names
COL4A2	309.013678	0.0
COL4A1	309.013678	0.0
ZEB2	309.013678	0.0
COL3A1	309.013678	0.0
MAN1A1	309.013678	0.0
...	...	...
SLC7A5	309.013678	0.0
CLDN6	309.013678	0.0
CDH3	309.013678	0.0
SLC38A1	309.013678	0.0
PODXL	309.013678	0.0

11525 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

871

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_up.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, 
                            GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_up)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_up.xlsx', save = True)

4.1.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_up.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_up)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_up.xlsx', save = True)

4.2 Downregulated

Code

%%R -i allDownSelected -i allGenes

allGenes_v <- c(allDownSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Code

%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13071,  3275,    10,  6197], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0051301	cell division	575	202	144.07	8.6e-11	8.572892e-11
1	GO:0006268	DNA unwinding involved in DNA replicatio...	22	18	5.51	3.6e-08	3.642153e-08
2	GO:0006271	DNA strand elongation involved in DNA re...	15	13	3.76	9.3e-07	9.334978e-07
3	GO:0007094	mitotic spindle assembly checkpoint sign...	46	30	11.53	7.2e-06	7.184653e-06
4	GO:1900264	positive regulation of DNA-directed DNA ...	13	11	3.26	1.1e-05	1.118943e-05
...	...	...	...	...	...	...	...
6269	GO:1904380	endoplasmic reticulum mannose trimming	13	0	3.26	1.00000	1.000000e+00
6270	GO:1904953	Wnt signaling pathway involved in midbra...	10	0	2.51	1.00000	1.000000e+00
6271	GO:1905038	regulation of membrane lipid metabolic p...	12	0	3.01	1.00000	1.000000e+00
6272	GO:2000303	regulation of ceramide biosynthetic proc...	11	0	2.76	1.00000	1.000000e+00
6273	GO:2000826	regulation of heart morphogenesis	10	0	2.51	1.00000	1.000000e+00

6274 rows × 7 columns

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

Code

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_down.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'blue')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='blue', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_down.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13424,  3348,    10,  1138], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_down.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'blue')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='blue', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_down.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13666,  3419,    10,   789], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_down.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'blue')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='blue', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_down.xlsx'), SE = markers)

Pathway annotation

4.2.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
names
COL4A2	309.013678	0.0
COL4A1	309.013678	0.0
ZEB2	309.013678	0.0
COL3A1	309.013678	0.0
MAN1A1	309.013678	0.0
...	...	...
SLC7A5	309.013678	0.0
CLDN6	309.013678	0.0
CDH3	309.013678	0.0
SLC38A1	309.013678	0.0
PODXL	309.013678	0.0

11525 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

871

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_down.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_down)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_down.xlsx', save = True)

4.2.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_down.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_down)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_down.xlsx', save = True)

4.3 All significant

Code

%%R -i allSelected -i allGenes

allGenes_v <- c(allUpSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Code

%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13071,  1638,    10,  5722], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0030199	collagen fibril organization	60	37	7.52	3.7e-19	3.692981e-19
1	GO:0030198	extracellular matrix organization	267	120	33.46	3.3e-17	3.278726e-17
2	GO:0001525	angiogenesis	450	137	56.39	4.0e-11	4.019062e-11
3	GO:0007155	cell adhesion	1176	283	147.37	1.1e-09	1.142650e-09
4	GO:0045766	positive regulation of angiogenesis	139	45	17.42	1.4e-09	1.440942e-09
...	...	...	...	...	...	...	...
6269	GO:2000601	positive regulation of Arp2/3 complex-me...	10	0	1.25	1.00000	1.000000e+00
6270	GO:2000765	regulation of cytoplasmic translation	26	0	3.26	1.00000	1.000000e+00
6271	GO:2000767	positive regulation of cytoplasmic trans...	13	0	1.63	1.00000	1.000000e+00
6272	GO:2001138	regulation of phospholipid transport	12	0	1.50	1.00000	1.000000e+00
6273	GO:2001140	positive regulation of phospholipid tran...	11	0	1.38	1.00000	1.000000e+00

6274 rows × 7 columns

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

Code

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'forestgreen')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_all.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13424,  1667,    10,   999], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'forestgreen')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_all.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13666,  1716,    10,   656], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'forestgreen')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_all.xlsx'), SE = markers)

Pathway annotation

4.3.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
names
COL4A2	309.013678	0.0
COL4A1	309.013678	0.0
ZEB2	309.013678	0.0
COL3A1	309.013678	0.0
MAN1A1	309.013678	0.0
...	...	...
SLC7A5	309.013678	0.0
CLDN6	309.013678	0.0
CDH3	309.013678	0.0
SLC38A1	309.013678	0.0
PODXL	309.013678	0.0

11525 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

871

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_all.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_all)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_all.xlsx', save = True)

4.3.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_all.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_all)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_all.xlsx', save = True)